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A multi-objective model for discovering high-quality knowledge based on data quality and prior knowledge

Author

Listed:
  • Qi Liu

    (Xi’an JiaoTong University
    The key lab of the ministry of education for process control and efficiency engineering)

  • Gengzhong Feng

    (Xi’an JiaoTong University
    The key lab of the ministry of education for process control and efficiency engineering)

  • Nengmin Wang

    (Xi’an JiaoTong University
    The key lab of the ministry of education for process control and efficiency engineering)

  • Giri Kumar Tayi

    (SUNY at Albany)

Abstract

Discovering knowledge from data means finding useful patterns in data, this process has increased the opportunity and challenge for businesses in the big data era. Meanwhile, improving the quality of the discovered knowledge is important for making correct decisions in an unpredictable environment. Various models have been developed in the past; however, few used both data quality and prior knowledge to control the quality of the discovery processes and results. In this paper, a multi-objective model of knowledge discovery in databases is developed, which aids the discovery process by utilizing prior process knowledge and different measures of data quality. To illustrate the model, association rule mining is considered and formulated as a multi-objective problem that takes into account data quality measures and prior process knowledge instead of a single objective problem. Measures such as confidence, support, comprehensibility and interestingness are used. A Pareto-based integrated multi-objective Artificial Bee Colony (IMOABC) algorithm is developed to solve the problem. Using well-known and publicly available databases, experiments are carried out to compare the performance of IMOABC with NSGA-II, MOPSO and Apriori algorithms, respectively. The computational results show that IMOABC outperforms NSGA-II, MOPSO and Apriori on different measures and it could be easily customized or tailored to be in line with user requirements and still generates high-quality association rules.

Suggested Citation

  • Qi Liu & Gengzhong Feng & Nengmin Wang & Giri Kumar Tayi, 0. "A multi-objective model for discovering high-quality knowledge based on data quality and prior knowledge," Information Systems Frontiers, Springer, vol. 0, pages 1-16.
    • Handle: RePEc:spr:infosf:v::y::i::d:10.1007_s10796-016-9690-6
    DOI: 10.1007/s10796-016-9690-6
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    References listed on IDEAS

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    1. Corne, David & Dhaenens, Clarisse & Jourdan, Laetitia, 2012. "Synergies between operations research and data mining: The emerging use of multi-objective approaches," European Journal of Operational Research, Elsevier, vol. 221(3), pages 469-479.
    2. César Guerra-García & Ismael Caballero & Mario Piattini, 2013. "Capturing data quality requirements for web applications by means of DQ_WebRE," Information Systems Frontiers, Springer, vol. 15(3), pages 433-445, July.
    3. Amir Parssian & Sumit Sarkar & Varghese S. Jacob, 2004. "Assessing Data Quality for Information Products: Impact of Selection, Projection, and Cartesian Product," Management Science, INFORMS, vol. 50(7), pages 967-982, July.
    4. de la Iglesia, B. & Richards, G. & Philpott, M.S. & Rayward-Smith, V.J., 2006. "The application and effectiveness of a multi-objective metaheuristic algorithm for partial classification," European Journal of Operational Research, Elsevier, vol. 169(3), pages 898-917, March.
    5. Tomo Popovic & Mladen Kezunovic & Bozo Krstajic, 2015. "Smart grid data analytics for digital protective relay event recordings," Information Systems Frontiers, Springer, vol. 17(3), pages 591-600, June.
    6. Atanu Lahiri & Debabrata Dey, 2013. "Effects of Piracy on Quality of Information Goods," Management Science, INFORMS, vol. 59(1), pages 245-264, June.
    7. Sikora, Riyaz & Piramuthu, Selwyn, 2007. "Framework for efficient feature selection in genetic algorithm based data mining," European Journal of Operational Research, Elsevier, vol. 180(2), pages 723-737, July.
    8. Qiang Yang & Xindong Wu, 2006. "10 Challenging Problems In Data Mining Research," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 597-604.
    9. Davidson, Ian & Tayi, Giri, 2009. "Data preparation using data quality matrices for classification mining," European Journal of Operational Research, Elsevier, vol. 197(2), pages 764-772, September.
    10. Jinwook Lee & András Prékopa, 2013. "Properties and calculation of multivariate risk measures: MVaR and MCVaR," Annals of Operations Research, Springer, vol. 211(1), pages 225-254, December.
    11. Szeto, W.Y. & Wu, Yongzhong & Ho, Sin C., 2011. "An artificial bee colony algorithm for the capacitated vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 215(1), pages 126-135, November.
    12. Naeem Khalid Janjua & Farookh Khadeer Hussain & Omar Khadeer Hussain, 2013. "Semantic information and knowledge integration through argumentative reasoning to support intelligent decision making," Information Systems Frontiers, Springer, vol. 15(2), pages 167-192, April.
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    1. Babak Daneshvar Rouyendegh & Kazim Topuz & Ali Dag & Asil Oztekin, 2019. "An AHP-IFT Integrated Model for Performance Evaluation of E-Commerce Web Sites," Information Systems Frontiers, Springer, vol. 21(6), pages 1345-1355, December.
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    4. Yalcin, Ahmet Selcuk & Kilic, Huseyin Selcuk & Delen, Dursun, 2022. "The use of multi-criteria decision-making methods in business analytics: A comprehensive literature review," Technological Forecasting and Social Change, Elsevier, vol. 174(C).

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